Dual-Function Hydrogel Dressings with a Dynamic Exchange of Iron Ions and an Antibiotic Drug for Treatment of Infected Wounds
Bibliographic record
Abstract
Bacterial infection is a major problem with diabetic wounds that may result in nonhealing chronic ulcers. Here, we report an approach to antibacterial hydrogel dressings for enhanced treatment of infected skin wounds. A fibrous hydrogel was derived from cellulose nanocrystals that were modified with dopamine and cross-linked with gelatin. The hydrogel was loaded with gentamicin, an antibiotic drug. Enhanced antibacterial hydrogel performance resulted from (i) a highly specific sequestration of Fe 3+ ions (much needed by bacteria) from the wound exudate and (ii) a dynamic exchange between gentamicin released from the hydrogel and Fe 3+ ions withdrawn from the wound exudate. Such exchange was possible due to the high value of the binding constant of Fe 3+ ions to dopamine. The hydrogel did not affect the metabolic activity of skin-related cells and showed enhanced antibacterial performance against common wound pathogens such as S. aureus and P. aeruginosa . Furthermore, it promoted healing of infected diabetic wounds due to a synergistic antibacterial effect providing the dynamic exchange between Fe 3+ ions and gentamicin. This work provides a strategy for the design of dual-function wound dressings, with both starving and killing bacteria and enhanced wound healing performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".